Active surveillance before radiotherapy — outcome and predictive factors for multiple biopsies before treatment
Bibliographic record
Abstract
INTRODUCTION: We aimed to investigate whether patients on active surveillance (AS) had worse outcomes than patients who received immediate treatment with radiotherapy and whether a Gleason grade progression on repeat biopsy influenced outcome. METHODS: From our institutional database, we identified 2001 patients treated between 2005 and 2019 with primary external beam radiation therapy or brachytherapy. Biochemical recurrence (BCR) was analyzed in relation to clinical factors such as a Gleason grade progression or having multiple biopsies vs. only one biopsy. Patients on AS were identified as those who had undergone ≥2 biopsies. We used log-rank tests for univariate analysis (UVA) and Cox regression analysis for multivariable analysis (MVA). RESULTS: Of 2001 patients, 374 (19%) patients had ≥2 biopsies before treatment, of which 48% presented with a Gleason grade progression of mostly to Gleason 3+4 (36%); 32% had a cancer volume increase on biopsy and 16% had no significant change on biopsy. For patients with ≥2 biopsies, median time from first biopsy to treatment was 22.0 months (interquartile range [IQR] 14.7-36.1). By UVA, patients with Gleason grade progression (n=105) had a worse BCR-free rate (p=0.02) than patients who had no grade progression on repeat biopsy or only one biopsy. On MVA, this effect was lost. Having ≥2 biopsies was not a significant negative prognostic factor on UVA (p=0.2) or MVA. CONCLUSIONS: In our experience, radiotherapy after a period of AS, even with Gleason grade progression, did not lead to worse outcomes compared to patients who had radiotherapy after only one biopsy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".